{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initialization\n",
    "\n",
    "Welcome to the first assignment of \"Improving Deep Neural Networks\". \n",
    "\n",
    "Training your neural network requires specifying an initial value of the weights. A well chosen initialization method will help learning.  \n",
    "\n",
    "If you completed the previous course of this specialization, you probably followed our instructions for weight initialization, and it has worked out so far. But how do you choose the initialization for a new neural network? In this notebook, you will see how different initializations lead to different results. \n",
    "\n",
    "A well chosen initialization can:\n",
    "- Speed up the convergence of gradient descent\n",
    "- Increase the odds of gradient descent converging to a lower training (and generalization) error \n",
    "\n",
    "To get started, run the following cell to load the packages and the planar dataset you will try to classify.\n",
    "\n",
    "---\n",
    "\n",
    "如果你完成了这个专业化的前一个课程，你可能按照我们的指导进行了权重初始化，并且到目前为止它已经成功了。但是，面对一个新的神经网络你究竟怎么选择初始化？在这个笔记中，你会看到不同的初始化会带来不同的结果。\n",
    "\n",
    "精心选择的初始化可以：\n",
    "\n",
    "- 加快梯度下降的收敛\n",
    "- 增加梯度下降收敛到较低的训练（和泛化）错误的几率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qVapEaI3XCfRrCcCeozOJTTxPq9AxuLvc2xsyIyue3Uen4upuz4UL5x66orQk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\nxSF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      "text/plain": [
       "<matplotlib.figure.Figure at 0xdd596d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import sklearn.datasets\n",
    "from init_utils import sigmoid, relu, compute_loss, forward_propagation, backward_propagation\n",
    "from init_utils import update_parameters, predict, load_dataset, plot_decision_boundary, predict_dec\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (7.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# load image dataset: blue/red dots in circles\n",
    "train_X, train_Y, test_X, test_Y = load_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You would like a classifier to separate the blue dots from the red dots.\n",
    "\n",
    "你需要一个分类器去区分蓝点和红点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Neural Network model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will use a 3-layer neural network (already implemented for you). Here are the initialization methods you will experiment with:  \n",
    "- *Zeros initialization* --  setting `initialization = \"zeros\"` in the input argument.\n",
    "- *Random initialization* -- setting `initialization = \"random\"` in the input argument. This initializes the weights to large random values.  \n",
    "- *He initialization* -- setting `initialization = \"he\"` in the input argument. This initializes the weights to random values scaled according to a paper by He et al., 2015. \n",
    "\n",
    "**Instructions**: Please quickly read over the code below, and run it. In the next part you will implement the three initialization methods that this `model()` calls."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model(X, Y, learning_rate = 0.01, num_iterations = 15000, print_cost = True, initialization = \"he\"):\n",
    "    \"\"\"\n",
    "    Implements a three-layer neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SIGMOID.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input data, of shape (2, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 for red dots; 1 for blue dots), of shape (1, number of examples)\n",
    "    learning_rate -- learning rate for gradient descent \n",
    "    num_iterations -- number of iterations to run gradient descent\n",
    "    print_cost -- if True, print the cost every 1000 iterations\n",
    "    initialization -- flag to choose which initialization to use (\"zeros\",\"random\" or \"he\")\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model\n",
    "    \"\"\"\n",
    "        \n",
    "    grads = {}\n",
    "    costs = [] # to keep track of the loss\n",
    "    m = X.shape[1] # number of examples\n",
    "    layers_dims = [X.shape[0], 10, 5, 1]\n",
    "    \n",
    "    # Initialize parameters dictionary.\n",
    "    if initialization == \"zeros\":\n",
    "        parameters = initialize_parameters_zeros(layers_dims)\n",
    "    elif initialization == \"random\":\n",
    "        parameters = initialize_parameters_random(layers_dims)\n",
    "    elif initialization == \"he\":\n",
    "        parameters = initialize_parameters_he(layers_dims)\n",
    "\n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "\n",
    "        # Forward propagation: LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID.\n",
    "        a3, cache = forward_propagation(X, parameters)\n",
    "        \n",
    "        # Loss\n",
    "        cost = compute_loss(a3, Y)\n",
    "\n",
    "        # Backward propagation.\n",
    "        grads = backward_propagation(X, Y, cache)\n",
    "        \n",
    "        # Update parameters.\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\n",
    "        \n",
    "        # Print the loss every 1000 iterations\n",
    "        if print_cost and i % 1000 == 0:\n",
    "            print(\"Cost after iteration {}: {}\".format(i, cost))\n",
    "            costs.append(cost)\n",
    "            \n",
    "    # plot the loss\n",
    "    plt.plot(costs)\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('iterations (per hundreds)')\n",
    "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "    plt.show()\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Zero initialization\n",
    "\n",
    "There are two types of parameters to initialize in a neural network:\n",
    "- the weight matrices $(W^{[1]}, W^{[2]}, W^{[3]}, ..., W^{[L-1]}, W^{[L]})$\n",
    "- the bias vectors $(b^{[1]}, b^{[2]}, b^{[3]}, ..., b^{[L-1]}, b^{[L]})$\n",
    "\n",
    "**Exercise**: Implement the following function to initialize all parameters to zeros. You'll see later that this does not work well since it fails to \"break symmetry\", but lets try it anyway and see what happens. Use np.zeros((..,..)) with the correct shapes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_zeros \n",
    "\n",
    "def initialize_parameters_zeros(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    parameters = {}\n",
    "    L = len(layers_dims)            # number of layers in the network\n",
    "    \n",
    "    for l in range(1, L):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.zeros((layers_dims[l], layers_dims[l-1]))\n",
    "        parameters['b' + str(l)] = 0\n",
    "        ### END CODE HERE ###\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 0.  0.  0.]\n",
      " [ 0.  0.  0.]]\n",
      "b1 = 0\n",
      "W2 = [[ 0.  0.]]\n",
      "b2 = 0\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_zeros([3,2,1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.  0.  0.]\n",
    " [ 0.  0.  0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.  0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using zeros initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.6931471805599453\n",
      "Cost after iteration 1000: 0.6931471805599453\n",
      "Cost after iteration 2000: 0.6931471805599453\n",
      "Cost after iteration 3000: 0.6931471805599453\n",
      "Cost after iteration 4000: 0.6931471805599453\n",
      "Cost after iteration 5000: 0.6931471805599453\n",
      "Cost after iteration 6000: 0.6931471805599453\n",
      "Cost after iteration 7000: 0.6931471805599453\n",
      "Cost after iteration 8000: 0.6931471805599453\n",
      "Cost after iteration 9000: 0.6931471805599453\n",
      "Cost after iteration 10000: 0.6931471805599455\n",
      "Cost after iteration 11000: 0.6931471805599453\n",
      "Cost after iteration 12000: 0.6931471805599453\n",
      "Cost after iteration 13000: 0.6931471805599453\n",
      "Cost after iteration 14000: 0.6931471805599453\n"
     ]
    },
    {
     "data": {
      "image/png": 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Nsu2gAWW/Ajafor2Hz13vJEmSRtfQ76GTJEnSmjHQSZIkdZyBTpIkqeMMdJIkSR1noJMk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\nJHWcgU6SJKnjDHSSJEkd9/8BL8NynepSukoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1124a898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.5\n",
      "On the test set:\n",
      "Accuracy: 0.5\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"zeros\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance is really bad, and the cost does not really decrease, and the algorithm performs no better than random guessing. Why? Lets look at the details of the predictions and the decision boundary:\n",
    "\n",
    "性能非常糟糕，成本并没有真正降低，算法也没有比随机猜测更好。为什么？让我们看看预测和决策边界的细节："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions_train = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0]]\n",
      "predictions_test = [[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      "  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print (\"predictions_train = \" + str(predictions_train))\n",
    "print (\"predictions_test = \" + str(predictions_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n"
     ]
    },
    {
     "data": {
      "image/png": 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QS10clTFkv0Vf5GcOcZySNKIYS5LOMvH3dew/dJ6Y2ERaPFSZF55+CHf3vB1C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\nqz8KpQGng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DeoeYn1WSZ7XaWkZrHn4GUa1ByCqcm9TU4ZWbF4lqlHY7+RhdO09tZebNi2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\nzRtXoGql8jxOSqdj70Vc832IpUKfVE0OyGD9r8Pp936jYvfdsO13ZN5J4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tk8V5VL9zUy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\nL9cuuHbTW8h1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1124a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with Zeros initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is predicting 0 for every example. \n",
    "\n",
    "In general, initializing all the weights to zero results in the network failing to break symmetry. This means that every neuron in each layer will learn the same thing, and you might as well be training a neural network with $n^{[l]}=1$ for every layer, and the network is no more powerful than a linear classifier such as logistic regression. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- The weights $W^{[l]}$ should be initialized randomly to break symmetry. \n",
    "- It is however okay to initialize the biases $b^{[l]}$ to zeros. Symmetry is still broken so long as $W^{[l]}$ is initialized randomly. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Random initialization\n",
    "\n",
    "To break symmetry, lets intialize the weights randomly. Following random initialization, each neuron can then proceed to learn a different function of its inputs. In this exercise, you will see what happens if the weights are intialized randomly, but to very large values. \n",
    "\n",
    "**Exercise**: Implement the following function to initialize your weights to large random values (scaled by \\*10) and your biases to zeros. Use `np.random.randn(..,..) * 10` for weights and `np.zeros((.., ..))` for biases. We are using a fixed `np.random.seed(..)` to make sure your \"random\" weights  match ours, so don't worry if running several times your code gives you always the same initial values for the parameters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_random\n",
    "\n",
    "def initialize_parameters_random(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)               # This seed makes sure your \"random\" numbers will be the as ours\n",
    "    parameters = {}\n",
    "    L = len(layers_dims)            # integer representing the number of layers\n",
    "    \n",
    "    for l in range(1, L):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l-1]) * 10\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 17.88628473   4.36509851   0.96497468]\n",
      " [-18.63492703  -2.77388203  -3.54758979]]\n",
      "b1 = [[ 0.]\n",
      " [ 0.]]\n",
      "W2 = [[-0.82741481 -6.27000677]]\n",
      "b2 = [[ 0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_random([3, 2, 1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 17.88628473   4.36509851   0.96497468]\n",
    " [-18.63492703  -2.77388203  -3.54758979]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[-0.82741481 -6.27000677]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using random initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\git_pro\\machine_learning_python_code\\吴恩达-深度学习-课后作业-答案-总结\\2_改善深层神经网络：超参数调试、正则化以及优化\\Week1 深层学习的实用\\assignment1\\init_utils.py:145: RuntimeWarning: divide by zero encountered in log\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n",
      "F:\\git_pro\\machine_learning_python_code\\吴恩达-深度学习-课后作业-答案-总结\\2_改善深层神经网络：超参数调试、正则化以及优化\\Week1 深层学习的实用\\assignment1\\init_utils.py:145: RuntimeWarning: invalid value encountered in multiply\n",
      "  logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: inf\n",
      "Cost after iteration 1000: 0.6239567039908781\n",
      "Cost after iteration 2000: 0.5978043872838292\n",
      "Cost after iteration 3000: 0.563595830364618\n",
      "Cost after iteration 4000: 0.5500816882570866\n",
      "Cost after iteration 5000: 0.5443417928662615\n",
      "Cost after iteration 6000: 0.5373553777823036\n",
      "Cost after iteration 7000: 0.4700141958024487\n",
      "Cost after iteration 8000: 0.3976617665785177\n",
      "Cost after iteration 9000: 0.39344405717719166\n",
      "Cost after iteration 10000: 0.39201765232720626\n",
      "Cost after iteration 11000: 0.38910685278803786\n",
      "Cost after iteration 12000: 0.38612995897697244\n",
      "Cost after iteration 13000: 0.3849735792031832\n",
      "Cost after iteration 14000: 0.38275100578285265\n"
     ]
    },
    {
     "data": {
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rLzjtNJg1K+9qzMxanwOdmbUJF14I06bBZZflXYmZWetzoDOzNuEb30i9Xi+8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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d86d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.83\n",
      "On the test set:\n",
      "Accuracy: 0.86\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"random\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you see \"inf\" as the cost after the iteration 0, this is because of numerical roundoff; a more numerically sophisticated implementation would fix this. But this isn't worth worrying about for our purposes. \n",
    "\n",
    "Anyway, it looks like you have broken symmetry, and this gives better results. than before. The model is no longer outputting all 0s. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 1 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 0 1 1\n",
      "  1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 1 1 0 0 0\n",
      "  0 0 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 0 0 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0 1 0 1\n",
      "  1 0 0 1 0 0 1 1 0 1 1 1 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 0 0 1 1 0 0 0 1 0\n",
      "  1 0 1 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1 0 1\n",
      "  0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 1 1 1 0 1 1 1 0 1 0 1 0 0 1 0 1 1 0 1 1\n",
      "  0 1 1 0 1 1 1 0 1 1 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 1 1 0 1 1 1 1 0 1 1 0 1\n",
      "  1 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 0 1\n",
      "  1 1 1 0]]\n",
      "[[1 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 1 1 1 0 0 0 0 1 0\n",
      "  1 1 0 0 1 1 1 1 1 0 1 1 1 0 1 0 1 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 1 1\n",
      "  1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 1 0 1 1 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print (predictions_train)\n",
    "print (predictions_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n"
     ]
    },
    {
     "data": {
      "image/png": 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DlVKTgUqt9UGl1BIgUWt9nfv9Z4CblFIPAS8CpwOXA+cd46wLMShFWPyY18ua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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a3438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with large random initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "- The cost starts very high. This is because with large random-valued weights, the last activation (sigmoid) outputs results that are very close to 0 or 1 for some examples, and when it gets that example wrong it incurs a very high loss for that example. Indeed, when $\\log(a^{[3]}) = \\log(0)$, the loss goes to infinity.\n",
    "- Poor initialization can lead to vanishing/exploding gradients, which also slows down the optimization algorithm. \n",
    "- If you train this network longer you will see better results, but initializing with overly large random numbers slows down the optimization.\n",
    "\n",
    "<font color='blue'>\n",
    "**In summary**:\n",
    "- Initializing weights to very large random values does not work well. \n",
    "- Hopefully intializing with small random values does better. The important question is: how small should be these random values be? Lets find out in the next part! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - He initialization\n",
    "\n",
    "Finally, try \"He Initialization\"; this is named for the first author of He et al., 2015. (If you have heard of \"Xavier initialization\", this is similar except Xavier initialization uses a scaling factor for the weights $W^{[l]}$ of `sqrt(1./layers_dims[l-1])` where He initialization would use `sqrt(2./layers_dims[l-1])`.)\n",
    "\n",
    "**Exercise**: Implement the following function to initialize your parameters with He initialization.\n",
    "\n",
    "**Hint**: This function is similar to the previous `initialize_parameters_random(...)`. The only difference is that instead of multiplying `np.random.randn(..,..)` by 10, you will multiply it by $\\sqrt{\\frac{2}{\\text{dimension of the previous layer}}}$, which is what He initialization recommends for layers with a ReLU activation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: initialize_parameters_he\n",
    "\n",
    "def initialize_parameters_he(layers_dims):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    layer_dims -- python array (list) containing the size of each layer.\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- python dictionary containing your parameters \"W1\", \"b1\", ..., \"WL\", \"bL\":\n",
    "                    W1 -- weight matrix of shape (layers_dims[1], layers_dims[0])\n",
    "                    b1 -- bias vector of shape (layers_dims[1], 1)\n",
    "                    ...\n",
    "                    WL -- weight matrix of shape (layers_dims[L], layers_dims[L-1])\n",
    "                    bL -- bias vector of shape (layers_dims[L], 1)\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(3)\n",
    "    parameters = {}\n",
    "    L = len(layers_dims) - 1 # integer representing the number of layers\n",
    "     \n",
    "    for l in range(1, L + 1):\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l-1]) * np.sqrt(2/layers_dims[l-1])\n",
    "        parameters['b' + str(l)] = np.zeros((layers_dims[l],1))\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 = [[ 1.78862847  0.43650985]\n",
      " [ 0.09649747 -1.8634927 ]\n",
      " [-0.2773882  -0.35475898]\n",
      " [-0.08274148 -0.62700068]]\n",
      "b1 = [[ 0.]\n",
      " [ 0.]\n",
      " [ 0.]\n",
      " [ 0.]]\n",
      "W2 = [[-0.03098412 -0.33744411 -0.92904268  0.62552248]]\n",
      "b2 = [[ 0.]]\n"
     ]
    }
   ],
   "source": [
    "parameters = initialize_parameters_he([2, 4, 1])\n",
    "print(\"W1 = \" + str(parameters[\"W1\"]))\n",
    "print(\"b1 = \" + str(parameters[\"b1\"]))\n",
    "print(\"W2 = \" + str(parameters[\"W2\"]))\n",
    "print(\"b2 = \" + str(parameters[\"b2\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "    <td>\n",
    "    **W1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 1.78862847  0.43650985]\n",
    " [ 0.09649747 -1.8634927 ]\n",
    " [-0.2773882  -0.35475898]\n",
    " [-0.08274148 -0.62700068]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b1**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]\n",
    " [ 0.]\n",
    " [ 0.]\n",
    " [ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **W2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[-0.03098412 -0.33744411 -0.92904268  0.62552248]]\n",
    "    </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **b2**\n",
    "    </td>\n",
    "        <td>\n",
    "    [[ 0.]]\n",
    "    </td>\n",
    "    </tr>\n",
    "\n",
    "</table> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to train your model on 15,000 iterations using He initialization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.8830537463419761\n",
      "Cost after iteration 1000: 0.6879825919728063\n",
      "Cost after iteration 2000: 0.6751286264523371\n",
      "Cost after iteration 3000: 0.6526117768893807\n",
      "Cost after iteration 4000: 0.6082958970572938\n",
      "Cost after iteration 5000: 0.5304944491717495\n",
      "Cost after iteration 6000: 0.4138645817071794\n",
      "Cost after iteration 7000: 0.3117803464844441\n",
      "Cost after iteration 8000: 0.23696215330322562\n",
      "Cost after iteration 9000: 0.18597287209206836\n",
      "Cost after iteration 10000: 0.15015556280371817\n",
      "Cost after iteration 11000: 0.12325079292273552\n",
      "Cost after iteration 12000: 0.09917746546525932\n",
      "Cost after iteration 13000: 0.08457055954024274\n",
      "Cost after iteration 14000: 0.07357895962677362\n"
     ]
    },
    {
     "data": {
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EBKCkpISSkpJKx5YvX562+1sYHhYtM5sJzHL3sxLfG/ApcIu7j01x/oPAD+5+TIVj/YGX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\n5mDNzMzMrIH9H1DBlJTK+TrAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115d36a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On the train set:\n",
      "Accuracy: 0.993333333333\n",
      "On the test set:\n",
      "Accuracy: 0.96\n"
     ]
    }
   ],
   "source": [
    "parameters = model(train_X, train_Y, initialization = \"he\")\n",
    "print (\"On the train set:\")\n",
    "predictions_train = predict(train_X, train_Y, parameters)\n",
    "print (\"On the test set:\")\n",
    "predictions_test = predict(test_X, test_Y, parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.minimum.reduce will be axis=0, not the current None, to match np.minimum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n",
      "D:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\ma\\core.py:6385: MaskedArrayFutureWarning: In the future the default for ma.maximum.reduce will be axis=0, not the current None, to match np.maximum.reduce. Explicitly pass 0 or None to silence this warning.\n",
      "  return self.reduce(a)\n"
     ]
    },
    {
     "data": {
      "image/png": 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VBHlCiNPWJ7XFNLgdfI/JxKkAvJSZM1QMZxPPW1X5OLX7ZBdRCCFOGgnyhBCnrTJHC0F4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\n/Sa5kr+kz+OVllLWOpoIiiJnGKO5JSZrTFYPZ2+2o/kY16+l9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CiKH/Z+9izwS0EQygiX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\nQ54kSVIOGfIkSZJy6P8BWEeE5mPKub8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11601be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"Model with He initialization\")\n",
    "axes = plt.gca()\n",
    "axes.set_xlim([-1.5,1.5])\n",
    "axes.set_ylim([-1.5,1.5])\n",
    "plot_decision_boundary(lambda x: predict_dec(parameters, x.T), train_X, train_Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**:\n",
    "- The model with He initialization separates the blue and the red dots very well in a small number of iterations.\n",
    "\n",
    "\n",
    "**观察：**\n",
    "- \"He initialization\"的模型在较少的迭代中很好地分离了蓝色和红色的点"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 5 - Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "You have seen three different types of initializations. For the same number of iterations and same hyperparameters the comparison is:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "        <td>\n",
    "        **Model**\n",
    "        </td>\n",
    "        <td>\n",
    "        **Train accuracy**\n",
    "        </td>\n",
    "        <td>\n",
    "        **Problem/Comment**\n",
    "        </td>\n",
    "\n",
    "    </tr>\n",
    "        <td>\n",
    "        3-layer NN with zeros initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        50%\n",
    "        </td>\n",
    "        <td>\n",
    "        fails to break symmetry\n",
    "        </td>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with large random initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        83%\n",
    "        </td>\n",
    "        <td>\n",
    "        too large weights \n",
    "        </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>\n",
    "        3-layer NN with He initialization\n",
    "        </td>\n",
    "        <td>\n",
    "        99%\n",
    "        </td>\n",
    "        <td>\n",
    "        recommended method\n",
    "        </td>\n",
    "    </tr>\n",
    "</table> "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember from this notebook**:\n",
    "- Different initializations lead to different results\n",
    "- Random initialization is used to break symmetry and make sure different hidden units can learn different things\n",
    "- Don't intialize to values that are too large\n",
    "- He initialization works well for networks with ReLU activations. \n",
    "\n",
    "---\n",
    "\n",
    "你应该记得从这个笔记本：\n",
    "\n",
    "- 不同的初始化导致不同的结果\n",
    "- 随机初始化用于破坏对称性，并确保不同的隐藏单元可以学习不同的东西\n",
    "- 不要初始化太大的值\n",
    "- \"He initialization\"适用于ReLU激活的网络\n"
   ]
  }
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